Konda Mani Saravanan

This paper uses phylogenomic analysis to examine sleep-related genes in LUCA, finding conserved genes linked to signaling, metabolism, and circadian rhythms, suggesting their fundamental role in evolution.

The paper uses phylogenomic methods to study sleep-related gene orthologs in Epsilonproteobacteria, identifying conserved proteins and their functional adaptations.

This paper uses phylogenomics to identify sleep-related genes in Lokiarchaeota, suggesting a conserved role for sleep in cellular maintenance and organismal fitness.

This study identifies conserved sleep-related gene orthologs in Chlamydomonas reinhardtii, suggesting evolutionary continuity of sleep functions across species.

The paper develops two GPCR-specific models, DeepGPCR_BC and DeepGPCR_RG, for binary classification and affinity prediction of ligand interactions, achieving better performance than existing tools.

This paper introduces DeepBindGCN, a model that predicts protein-ligand interactions using graph convolutional networks and molecular vectors, achieving high accuracy in benchmark datasets.

This paper generates and screens bioactive peptides using deep learning models, demonstrating their potential for therapeutic applications.

The paper generates and screens a bioactive peptide against Xanthine oxidase using LSTM_Pep and DeepPep, achieving a 64.32% inhibitory activity with an IC50 of 3.76mg/mL.

This paper develops a machine learning model to identify iPS progenitor cells using microscopic image analysis, achieving over 52% precision in detecting these rare cells during early reprogramming.

The paper distinguishes native IDP regions from predicted ones using sequence features, finding higher asparagine occurrence and specific pentapeptide combinations in native IDPs.